We introduce a new approach to algorithmic investment management that yields profitable automated trading strategies. This trading model design is the result of a path of investigation that was chosen nearly three decades ago. Back then, a paradigm change was proposed for the way time is defined in financial markets, based on intrinsic events. This definition lead to the uncovering of a large set of scaling laws. An additional guiding principle was found by embedding the trading model construction in an agent-base framework, inspired by the study of complex systems. This new approach to designing automated trading algorithms is a parsimonious method for building a new type of investment strategy that not only generates profits, but also provides liquidity to financial markets and does not have a priori restrictions on the amount of assets that are managed.

Notable quotations from the academic research paper:

"To summarize, our aim is to develop trading models based on parsimonious, self-similar, modular, and agent-based behavior, designed for multiple time horizons and not purely driven by trend following action. The intellectual framework unifying these angles of attack is outlined in Section 3 of source research paper. The result of this endeavor are interacting systems that are highly dynamic, robust, and adaptive. In other words, a type of trading model that mirrors the dynamic and complex nature of financial markets. The code can be download from GitHub [The Alpha Engine: Designing an Automated Trading Algorithm Code. https://github.com/AntonVonGolub/Code/blob/master/code.java. Accessed: 2017-01-04. 2017]

The Alpha Engine is a counter-trending trading model algorithm that provides liquidity by opening a position when markets overshoot, and manages positions by cascading and de-cascading during the evolution of the long coastline of prices, until it closes in a prot. The building blocks of the trading model are:

- an endogenous time scale called intrinsic time that dissects the price curve into directional changes and overshoots;
- patterns, called scaling laws that hold over several orders of magnitude, providing an analytical relationship between price overshoots and directional change reversals;
- coastline trading agents operating at intrinsic events, defined by the event based language;
- a probability indicator that determines the sizing of positions, by identifying periods of market activity that deviate from normal behavior;
- skewing of cascading and de-cascading designed to mitigate the accumulation of large inventory sizes during trending markets;
- the splitting of directional change and, consequently, overshoot thresholds into upwards and downwards components, i.e., the introduction of asymmetric thresholds."